GPT-6 Luna (high), Codex vs. GPT-5.6 Sol (medium), Codex
GPT-6 Luna (high), Codex against GPT-5.6 Sol (medium), Codex: Geospatial Agent Index, each evaluation, cost, time and tokens, on the same tasks and limits. Methodology.
Side by side
| Metric | GPT-6 Luna (high), Codex | GPT-5.6 Sol (medium), Codex |
|---|---|---|
| Geospatial Agent Index: Text only | 71 | 100, not every task attempted yet (19 of 488 tasks attempted) |
| Geospatial Agent Index: Text + vision | 70 | Still to run: Earth-Bench V2, GeoBenchX, GS-QA2, Vision agents |
| Earth-Bench V2 score | 63 | No data |
| GeoBenchX score | 74 | No data |
| GS-QA2 score | 77 | 100 |
| Vision agents score | 67 | No data |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 96 | No data |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 100 | No data |
| Raster and remote sensing score | 73 | 100 |
| Vector and overlay score | 77 | No data |
| Climate and time series score | 66 | No data |
| Spatial statistics and interpolation score | 54 | No data |
| Mapping and cartography score | 79 | No data |
| Recognising infeasible requests score | 75 | No data |
| Cost per task | $0.0040 | $0.060 |
| Time per task (at 100 output tokens/s) | 0.7 min | 0.2 min |
| Tokens per attempt | 120k | 66k |
| Turns per attempt | 7.4 | 6.1 |
| Attempts scored | 488 | 19 |
| Input price per 1M tokens | $0.10 | $4.00 |
| Output price per 1M tokens | $0.50 | $20.00 |
| Developer | OpenAI | OpenAI |
* Not every task attempted yet: the score may still change. What the asterisk means.
Charts
Not yet ranked. GPT-5.6 Sol (medium), Codex: Earth-Bench V2 not started; GeoBenchX not started; 19 of 221 GS-QA2 tasks attempted. Grey placeholders mark these entries in the index, cost and output token charts; their height is not a value. Bars appear once every task has an attempt. Results so far are in the side-by-side table.
Geospatial Agent Index
Equal average of Earth-Bench V2, GeoBenchX and GS-QA2, 0 to 100 · Higher is better
Top eight. Every model is in the table.
Data table: Geospatial Agent Index
| Model | Developer | Geospatial Agent Index | Status | Evaluations scored | Tasks attempted |
|---|---|---|---|---|---|
| GPT-6 Luna (high), Codex | OpenAI | 71 | Complete | 3 of 3 | 488 of 488 tasks |
Cost per task
Average cost per task (USD) · Lower is better
Top eight. Every model is in the table.
How to read this chart
Not shown: not yet ranked. 1 entries without an attempt on every index task. What is still to run is on the models page.
Data table: Cost per task
| Model | Developer | Cost per task | Tasks attempted |
|---|---|---|---|
| GPT-6 Luna (high), Codex | OpenAI | $0.0040 | 488 of 488 tasks |
Output tokens per task
Average output tokens per attempt, reasoning included · Lower is better
Top eight. Every model is in the table.
How to read this chart
Not shown: not yet ranked. 1 entries without an attempt on every index task. What is still to run is on the models page.
Data table: Output tokens per task
| Model | Developer | Output tokens per task | Tasks attempted |
|---|---|---|---|
| GPT-6 Luna (high), Codex | OpenAI | 3k | 488 of 488 tasks |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, GPT-6 Luna (high), Codex or GPT-5.6 Sol (medium), Codex?
Which is cheaper per task, GPT-6 Luna (high), Codex or GPT-5.6 Sol (medium), Codex?
Which writes fewer output tokens per task, GPT-6 Luna (high), Codex or GPT-5.6 Sol (medium), Codex?
More: GPT-6 Luna (high), Codex · GPT-5.6 Sol (medium), Codex · All comparisons